Self-defining tree-like classifiers for interpretation of Raman spectroscopic experiments

In this contribution, a technique is proposed to create a data-driven interpretation of a given chemometric analysis of a Raman dataset. In real-world applications, the chemometric analysis is fixed by some external measurement, for example, a legal standard, or a set of fixed goals. Thus, the exact chemometric work flow is fixed because of those goals. However, a further optimization, for example, of the measurement itself relies on an interpretation of the resulting chemometric analysis. For this purpose, a data-driven analysis of the chemometric analysis itself has to be carried out. This contribution tries to achieve that goal by combining two methods. The first proposed technique is the calculation of the so-called importance map, which allows the computation of the importance of every channel for a given model and a given dataset. This importance map is constructed after the complete result of an out-of-bag (OOB) validation and the decrease of accuracy by randomized channels. The second technique is the growing of the optimal decision tree based on the action of the model used for chemometric analysis. By this way, a clustering is achieved on which by binary classifiers, the optimal decision tree is grown. This tree can be interpreted as dividing the whole dataset into meta clusters. Combining these techniques, a new way of interpreting datasets based on the chosen model is proposed. This combination closes the gap between chemometric analysis and the need for interpretation.